John Edward. O'Malley et al.

Patent Trials and Appeals Board

Oct 7, 2019

13111804 - (D) (P.T.A.B. Oct. 7, 2019)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
13/111,804 05/19/2011 John Edward O'Malley 11360.0467-00000 6212
96592 7590 10/07/2019
FIS/FINNEGAN, HENDERSON, FARABOW,
GARRETT & DUNNER LLP
901 NEW YORK AVENUE
WASHINGTON, DC 20001-4413
EXAMINER
ROSS, SCOTT M
ART UNIT PAPER NUMBER
3623
NOTIFICATION DATE DELIVERY MODE
10/07/2019 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
jeffrey.berkowitz@finnegan.com
michael.wrobel@fisglobal.com
regional-desk@finnegan.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte JOHN EDWARD O'MALLEY,
MARIA MARGARET LOUGHLIN, YAKOV I. SVERDLOV,
and MARK JEFFREY WAKS
____________________

Appeal 2018-003521
Application 13/111,804
Technology Center 3600
____________________

Before JENNIFER L. McKEOWN, LINZY T. McCARTNEY, and
JESSICA C. KAISER, Administrative Patent Judges.

McCARTNEY, Administrative Patent Judge.

DECISION ON APPEAL
Appellant1 seeks review under 35 U.S.C. § 134 of the Examiner’s
final rejection of claims 1–34. We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.

1 Appellant identifies the real party in interest as FIS Financial Compliance
Solutions, LLC. Appeal Brief 3, filed October 21, 2016 (“App. Br.”).
Appeal 2018-003521
Application 13/111,804

2
BACKGROUND
This patent application concerns “integrating, analyzing and
publishing information potentially indicative of fraudulent activity that is
received from a plurality of source systems.” Specification 1:7–8, filed May
19, 2011 (“Spec.”). Claims 1, 18, and 32 are independent. Claim 1 illustrates
the claimed subject matter:
1. A computer system comprising:
a processor configured to execute an operating system that
manages at least a portion of the hardware elements included in
the computer system, the processor having access to a networked
scenario database comprising a plurality of pattern data
associated with fraudulent activity categories;
a network interface in communication with the processor;
and
a memory device in communication with the processor
and configured to store instructions,
wherein, when the processor executes the instructions, the
processor:
stores, in the memory device, a plurality of data points
identified as pertinent to potentially fraudulent activity, the
plurality of data points including a first data point received from
a first source system and a second data point received from a
second source system of a different type from the first source
system, wherein the source systems are selected from: a system
providing transactional data points, a system providing
identification data points, and a system providing fraud-related
data points indicative of potentially fraudulent activity;
receives, by a producer interface connected over the
network interface to a plurality of source systems, requests to
store the plurality of data points, the plurality of source systems
including the first source system and the second source system;
compares, by a fraud exchange engine executed by the
processor, the first and second data points to at least one of the
Appeal 2018-003521
Application 13/111,804

3
pattern data, wherein the fraud exchange engine is configured to
execute at least one of: event abstraction, modeling data point
hierarchies, or abstracting data point-driven processes;
produces, if the first and second data points match the at
least one of the pattern data, a relational data point corresponding
to the first and second data points, wherein the relational data
point is produced based on the types of source systems for the
first and second data points; and
receives, by a consumer interface connected over the
network interface to at least one target system, a request to
retrieve at least one of a set of the plurality of data points and the
at least one relational data point,
wherein the fraud exchange engine communicates with the
consumer interface via the network interface to retrieve the at
least one relational data point,
wherein the fraud exchange engine causes the producer
interface to output, via the network interface, at least a first alert,
the first alert implemented as executable code and corresponding
to the relational data point, and the first alert being sent to one or
more business systems, and
wherein the first alert, when received by the one or more
business systems, causes the one or more business systems to
suspend, cancel, or release a transaction.
App. Br. 30–31.

Appeal 2018-003521
Application 13/111,804

4

REJECTIONS2
Claims Basis References
1–14, 16–27, and
29–34 § 103(a)
Ganti,3 Larkin,4 and
Grell5
15 and 28 § 103(a) Ganti, Larkin, Grell, and Hinton6
DISCUSSION
We have reviewed the Examiner’s rejections and Appellant’s
arguments, and we disagree with Appellant that the Examiner erred. As
consistent with the discussion below, we adopt the Examiner’s reasoning,
findings, and conclusions on pages 25–43 of the Final Office Action mailed
May 23, 2016 (“Final Act.”) and pages 3–14 of the Examiner’s Answer
mailed December 21, 2017. We address Appellant’s arguments in turn.
First, Appellant contends that the Examiner’s combination of Ganti,
Larkin, and Grell fails to teach or suggest the following limitation recited in
claim 1:
produces, if the first and second data points match the at least one
of the pattern data, a relational data point corresponding to the
first and second data points, wherein the relational data point is
produced based on the types of source systems for the first and
second data points.
App. Br. 24–27. Appellant argues that Ganti “is silent with respect to” and
“nowhere” teaches this limitation. App. Br. 25 (emphasis omitted). In

2 The Examiner withdrew the rejection of claims 1–34 under 35 U.S.C.
§ 101. Answer 3, mailed December 21, 2017 (“Ans.”).
3 Ganti (US 2012/0158585 A1; June 21, 2012).
4 Larkin (WO 2010/095122 A1; August 26, 2010).
5 Grell et al. (US 2010/0287361 A1; November 11, 2010).
6 Hinton et al. (US 2008/0010665 A1; January 10, 2008).
Appeal 2018-003521
Application 13/111,804

5
particular, Appellant contends that Ganti’s “mere mention of an ‘alarm’ does
[not] constitute or suggest the production of a relational data point, as
claimed.” App. Br. 25. Appellant also asserts that “[t]here is no discussion in
Ganti of the relational data point being produced ‘based on the types of
source systems,’ as recited in claim 1.” Reply Brief 5, filed February 16,
2018 (“Reply Br.”) (emphases omitted); see also App. Br. 25. Appellant
argues that neither Larkin nor Grell remedies these deficiencies. See App.
Br. 26–27.
Appellant has not persuaded us that the Examiner erred. The
Examiner found—and we agree—that Ganti teaches producing the recited
“relational data point” because Ganti discloses processing transactions (first
and second data points) using rules (pattern data), and if the transactions
satisfy the rules (if the first and second data points match the at least one of
the pattern data), producing an alarm (relational data point), in some cases
by aggregating the transactions. See, e.g., Ans. 4–5 (citing Ganti ¶¶ 48–56,
69, 86); see also Ganti ¶¶ 36, 42–47, 57, 58, 60–66, 95–99, 105–111.
Although Appellant contends that Ganti’s alarm is not a relational
data point, the written description indicates that a relational data point
includes a data point produced by aggregating data points or matching data
points to a pattern of fraudulent behavior. See, e.g., Spec. 28:15–18 (“[T]he
analyses of the first and second data points to produce the relational data
point (in act 306) may include accumulating, aggregating, trending and/or
adjusting.”), 28:28–30 (“In at least one embodiment, trending of the first and
second data point may be performed based on the two data points meeting a
pattern of behavior indicating fraudulent activity.”), 29:14–16 (“In one
example, the alert may correspond to the relational data point after
Appeal 2018-003521
Application 13/111,804

6
aggregating the first data point and the second data point into the relational
data point.”). Ganti teaches aggregating transactions into an alarm. See, e.g.,
Ganti ¶ 69 (“By processing each of transactions T1-T4 using rules in select
libraries 610, zero or more alarms may be generated. . . . An alarm may be
an aggregation of one or more transactions . . . that share a common
attribute.”). Ganti also teaches producing an alarm when transactions match
rules that indicate fraudulent activity. See, e.g., Ganti ¶¶ 54 (explaining that
“[p]attern recognition rules” include “rules indicative of patterns associated
with fraudulent transactions”), 57 (explaining that “[a] rule is triggered when
the transaction satisfies the rule”). Given these disclosures and the
description of relational data points in the written description, we agree with
the Examiner that Ganti’s alarms are relational data points.
As for Appellant’s argument that Ganti does not teach that “the
relational data point is produced based on the types of source systems for the
first and second data points,” the Examiner concluded that this limitation
includes producing relational data points using data points from different
types of source systems. See, e.g., Ans. 6–7. Although Appellant argues in
passing that “[a] system that processes transactions is different from source
systems that produce relational data points,” Reply Br. 5, Appellant has not
explicitly challenged the Examiner’s interpretation of the limitation.7
Ganti teaches that “the relational data point is produced based on the
types of source systems for the first and second data points” under the
Examiner’s interpretation of the limitation. Ganti teaches receiving

7 We note that the limitation recites that “the relational data point is
produced based on the types of source systems,” not that the source systems
themselves produce relational data points as asserted by Appellant. App. Br.
31.
Appeal 2018-003521
Application 13/111,804

7
transaction information from different types of source systems. See, e.g.,
Ganti ¶¶ 42 (disclosing receiving black and white lists and “historical
records of transactions from the merchant or merchant device” and
information about “transactions that were processed by a system other than
fraud management system 230”), 95 (disclosing receiving, among other
things, “information regarding a transaction from another consumer” and
“information regarding a transaction from another merchant”). Ganti also
teaches processing transaction information using source-specific rules. See,
e.g., Ganti ¶¶ 50–56 (describing exemplary rules, including merchant- and
customer-specific rules), 57 (providing an example of a transaction
satisfying a rule), 60–64 (describing various types of transaction information
referred to as “transaction attributes,” including merchant and consumer
information), 65–66 (disclosing selecting the rules used to process
transactions based in part on transaction attributes), 98 (explaining that
transactions can be processed using rules). In light of these teachings, among
others, we agree with the Examiner that Ganti teaches that “the relational
data point is produced based on the types of source systems for the first and
second data points” under the Examiner’s interpretation of the limitation.
Second, Appellant contends that Larkin fails to teach or suggest “the
first alert implemented as executable code and corresponding to the
relational data point, and the first alert being sent to one or more business
systems” and “wherein the first alert, when received by the one or more
business systems, causes the one or more business systems to suspend,
cancel, or release a transaction.” App. Br. 26. Appellant argues that Larkin’s
“[m]ere reception of interrupt data does not constitute or suggest an alert
Appeal 2018-003521
Application 13/111,804

8
based on a produced relational data point causing suspension, cancelation, or
release of a transaction, as claimed.” App. Br. 26.
Appellant has not persuaded us that the Examiner erred. The
Examiner concluded that these limitations would have been obvious over a
combination of Ganti’s and Larkin’s teachings. See, e.g., Final Act. 32–33;
Ans. 7–8. The Examiner found that Ganti teaches the recited “relational data
point,” “first alert corresponding to the relational data point,” and “first alert
being sent to one or more business systems.” See, e.g., Final Act. 31–33
(finding that Ganti’s alarm and fraud score respectively teach the recited
relational data point and first alert and finding that Ganti’s outputting of a
fraud score to a merchant teaches sending the first alert to one or more
business systems); Ans. 7 (making similar findings). The Examiner found
that Larkin teaches implementing an alert as executable code, where the
code causes one or more business systems to suspend, cancel, or release a
transaction. See, e.g., Ans. 8 (finding, among other things, that Larkin
teaches receiving interrupt data and interrupting the processing of an
electronic transaction); Final Act. 32 (making similar findings). The
Examiner concluded that a combination of these teachings would have
rendered the disputed limitations obvious, see, e.g., Final Act. 32–33, and
Appellant’s arguments against Larkin alone has not convinced us that the
Examiner erred. “[O]ne cannot show non-obviousness by attacking
references individually where, as here, the rejections are based on
combinations of references.” In re Keller, 642 F.2d 413, 426 (CCPA 1981).
Third, Appellant argues for the first time in the Reply Brief that the
Examiner erroneously found that Ganti’s fraud score is the recited first alert.
See Reply Br. 5. Appellant also argues for the first time in the Reply Brief
Appeal 2018-003521
Application 13/111,804

9
that the Examiner erroneously found that Ganti’s disclosure of using the
alert to assist a merchant in determining whether to accept, deny, or fulfill a
transaction is the same as causing a system to suspend a transaction as
required by claim 1. See Reply Br. 5.
Appellant forfeited these arguments by failing to raise them in the
Appeal Brief. The Examiner first made the finding that Appellant challenges
in the Final Office Action. Compare Final Act. 32 (finding that Ganti
teaches a first alert that influences business systems to suspend, cancel, or
release a transaction because Ganti teaches “fraud scores [that] assist
merchant[s] in determining whether to accept, deny, or fulfill [a]
transaction” (emphasis omitted)), with Reply Br. 5 (stating that “the
Examiner asserts that ‘fraud scores [which] assist merchant in determining
whether to accept, deny, or fulfill transaction’ constitutes the claimed ‘first
alert’ that ‘causes the one or more business systems to suspect, cancel, or
release a transaction.’” (alteration in original)). Yet Appellant waited until
the Reply Brief to explicitly raise these arguments and did not explain why
Appellant failed to raise the arguments earlier. See Reply Br. 5. Appellant
has therefore forfeited these arguments. See 37 C.F.R. §§ 41.37(c)(1)(iv)
(explaining that with exceptions inapplicable here, “any arguments or
authorities not included in the appeal brief will be refused consideration by
the Board for purposes of the present appeal”), 41.41(b)(2) (“Any argument
raised in the reply brief which was not raised in the appeal brief . . . will not
be considered by the Board for purposes of the present appeal, unless good
cause is shown.”).
Even if Appellant had timely raised these arguments, we would have
found them unpersuasive. As discussed above, the Examiner found that the
Appeal 2018-003521
Application 13/111,804

10
recited first alert would have been obvious over the combined teachings of
Ganti and Larkin. See, e.g., Final Act. 32–33; Ans. 7–8. In particular, the
Examiner found that Larkin teaches implementing an alert as executable
code, where the code causes one or more business systems to suspend,
cancel, or release a transaction. See, e.g., Final Act. 32; Ans. 8. Appellant’s
argument that Ganti’s fraud score alone does not teach or suggest the recited
first alert does has not persuaded us that the Examiner erred. See Keller, 642
F.2d at 426.
Fourth, Appellant argues that Grell does not teach “compar[ing], by a
fraud exchange engine executed by the processor, the first and second data
points to at least one of the pattern data, wherein the fraud exchange engine
is configured to execute at least one of: event abstraction, modeling data
point hierarchies, or abstracting data point-driven processes.” App. Br. 27. In
the Appeal Brief, Appellant summarizes the Examiner’s findings about Grell
and states that “Appellant disagrees with [the Examiner’s] assertions.” App.
Br. 27. Appellant also emphasizes certain language in the disputed limitation
and argues, without explanation, that Grell fails to teach the emphasized
language. See App. Br. 27. In the Reply Brief, Appellant asserts for the first
time that Grell’s “complex event processing is not event abstraction.” Reply
Br. 5.
Appellant has not persuaded us that the Examiner erred. The
Examiner concluded that this limitation would have been obvious over a
combination of Ganti’s and Grell’s teachings. See Final Act. 30–33; Ans. 9–
11. The Examiner found that Ganti teaches a fraud exchange engine that
compares first and second data points to pattern data. See, e.g., Final Act.
30–31 (finding that Ganti teaches the recited fraud exchange engine because
Appeal 2018-003521
Application 13/111,804

11
Ganti discloses a component that processes transactions using rules). The
Examiner found that Grell teaches using complex event processing to
execute at least one of event abstraction, modeling data point hierarchies, or
abstracting data point-driven processes. See, e.g., Final Act. 33 (finding that
Grell teaches executing the recited operations because Grell teaches that
complex event processing is used in fraud detection and that complex event
processing operations include abstracting events). The Examiner concluded
that a combination of these teachings would have rendered the disputed
limitation obvious. See Final Act. 30–33; Ans. 9–11.
Although in the Appeal Brief Appellant disagrees with the Examiner’s
findings about Grell, Appellant does not explicitly state in the Appeal Brief
the basis for the disagreement or persuasively explain why the Examiner’s
combination of Ganti and Grell fails to teach or suggest the disputed
limitation. See App. Br. 27. We thus find Appellant’s assertions in the
Appeal Brief unpersuasive. See Keller, 642 F.2d at 426. As for the Reply
Brief, Appellant forfeited the argument that Grell’s complex event
processing is not event abstraction. The Examiner found in the Final Office
Action that Grell teaches using complex event processing to execute at least
one of event abstraction, modeling data point hierarchies, or abstracting data
point-driven processes. See, e.g., Final Act. 33. But Appellant waited until
the Reply Brief to argue that Grell’s complex event processing is not event
abstraction and did not explain the delay in raising the argument. See Reply
Br. 6. Appellant has thus forfeited this argument. See 37 C.F.R.
§§ 41.37(c)(1)(iv), 41.41(b)(2). In any event, Grell discloses that “[c]omplex
event processing is a method of computing that performs operations on
complex events,” “examples of complex event processing include . . . fraud
Appeal 2018-003521
Application 13/111,804

12
detection,” and “[o]perations performed on the events in a complex event
processor can include . . . abstracting the events.” Grell ¶ 1 (emphasis
added). Given these disclosures, Appellant’s arguments do not persuade us
of any error in the Examiner’s findings about Grell. See Final Act. 33.
Fifth, Appellant argues that the Examiner “pointed out nothing in the
prior art that would motivate one of skill in the art to modify the teachings of
the prior art to achieve the claimed combination. Accordingly, no reason has
been articulated as to why one of skill in the art would find the claimed
combination obvious in view of the prior art.” App. Br. 28.
Appellant has not persuaded us that the Examiner erred. The
Examiner explicitly provided motivations for combining the teachings of
Ganti, Larkin, and Grell to arrive at the claimed invention. The Examiner
found that one of ordinary skill in the art would have been motivated to
modify Ganti’s fraud management system to include Larkin’s interrupt data
because doing so would provide Ganti’s system “with instructions to
interrupt a processing of the transaction.” Ans. 12 (emphasis omitted). The
Examiner found that one of ordinary skill in the art would have been further
motivated to modify this combination with Grell’s complex event processing
because the modification “would help Ganti’s fraud detection system derive
or infer significant output events from a large number of input events.” Ans.
13 (quotation mark and emphasis omitted).
For the above reasons, we find unpersuasive Appellant’s arguments
that the Examiner has not shown that the combination Ganti, Larkin, and
Grell teaches or suggests the limitations recited in claim 1. We thus sustain
the Examiner’s rejection of claim 1. Because Appellant has not provided
Appeal 2018-003521
Application 13/111,804

13
separate arguments for claims 2–34, we also sustain the Examiner’s
rejections of these claims.

CONCLUSION
Claims Basis References Affirmed Reversed
1–14, 16–27, and
29–34 § 103(a)
Ganti, Larkin,
and Grell
1–14, 16–
27, and
29–34

15 and 28 § 103(a)
Ganti, Larkin,
Grell, and
Hinton
15 and 28
Outcome 1–34

No period for taking any action in connection with this appeal may be
extended under 37 C.F.R. § 1.136(a). See 37 C.F.R. § 41.50(f).

AFFIRMED